بررسی اثرات بر تنوع منابع تحقیق و توسعه و سرمایه انسانی بر عملکرد صنعتی

This study conducts a 9-year longitudinal analysis on the effects of diversity of R&D sources, diversity of human capital, innovation infrastructure and academic knowledge on industrial performance. Here, I suggest there is an inverse U-shaped relationship between diversity of R&D sources and industrial performance. Industrial performance is also related to diversity of human capital in a curvilinear (inverted U-shaped) manner. Moreover, innovation infrastructure negatively moderates the effect of diversity of R&D sources on industrial performance, while academic knowledge also negatively moderates the effect of diversity of R&D human capital on industrial performance. The fixed- and random-effects regressions are used to test the hypotheses in a panel data of 315 industry-year cases and the findings support our prediction. The results of this study can help reconciling contradictory findings from previous studies by demonstrating the potential impact of diversity on industrial performance.

Why do some industries increase their performance in overall production over time, while in others, performance gradually declines? Innovations have become an increasing important means of developing and maintaining industrial competitive advantage [1]. The accumulation of knowledge capital through research and development (R&D) could persistently strengthen industrial competitiveness. R&D is the core of innovation, and its importance is widely recognized. The scope and breadth of R&D activities are varied and evolve with the passage of time. To accumulate the necessary knowledge, industrial organizations turn to external activities from other R&D units [2]. Keeping pace with a dynamic and uncertain environment commonly requires combing knowledge from multiple sources. Knowledge sources and R&D human capital are important means by which industry managers identify and gain access to relevant knowledge. Successful firms may not only rely on internal developments within their boundaries, but also choose to acquire knowledge and capabilities from other institutions of R&D sources [3] and [4]. Thus, to cope with R&D uncertainty and to increase their productivity, firms increasingly rely on external partners to tap into sources of new knowledge. Previous research studies have shown that a broad search for different types of information from suppliers and customers allows firms to build a new knowledge stock within the firms [5] and [6]. Research has also shown that firms engaged in R&D activities with different types of partners are able to alleviate increasing costs of innovation [7] and are more likely to effectively capture the benefits of their innovative effort [8]. External R&D units allow firms to tap into sources of knowledge outside their own realm. They allow firms to access information and knowledge they lack without the cost of producing and developing solely themselves. Given the increasing costs of R&D activities, R&D sources have become increasingly important [9]. A changing competitive environment induces shorter technological life cycles and relatively higher development costs. For example, Taiwan has some disadvantages in developing its manufacturing industry because it has a shorter industrialized history, a smaller R&D scale, and weaker technical capabilities. Therefore, in the institutional context of Taiwan, the Taiwanese government controls the channeling of resources from society to industry by undertaking core R&D itself [10] and [11]. In order to promote industrial collaboration for information exchange while sharing the costs and risks of technological development, the Taiwanese government created a unique division for R&D between government and industry in the case of the IT industry [12]. With rich R&D partnerships in an industry, the variety and diversity of knowledge sources have also increased. Firms can establish multiple R&D partnerships simultaneously with a broad range of knowledge sources. Established in 1987, TSMC (Taiwan Semiconductor Manufacturing Company Limited) is the world's first dedicated semiconductor foundry. Before the 1980s, suppliers of chips mostly spent large sums to design chips themselves and churn them out in their own factories. TSMC came up with a specialized foundry for companies willing to outsource manufacturing to an unknown Taiwan company. The option erased an enormous financial barrier to entry for engineer entrepreneurs, and created fortunes for Qualcomm and Boardcom. Older industry giants like Philips and AMD turned to TSMC, too [13]. As the founder and a leader of the dedicated IC foundry segment, TSMC has devoted significant resources and R&D investment to process technology development and has been able to develop advanced process technologies independently because of accumulated technological knowledge and competence. With a diverse global customer base, TSMC-manufactured microchips are used in a broad variety of applications that cover various segments of the computer, communications, consumer, and other electronics markets [14]. The foundry industry as characterized by its intensive capital expenditure, advanced technology investment, and cyclical market demand required agile and flexible strategies for production capacity planning and industrial growth [15]. If TSMC does not anticipate these changes in technologies and rapidly develops new and innovative technologies, they may be unable to remain a technological leader in semiconductor industry. In 2010 and 2011, the total R&D expenses were US$1020 million and US$1118 million, which represented 7.1% and 7.9% of net sales, respectively. TSMC will continue to invest significant amounts on R&D to maintain a leading position in the development of advanced process technologies. TSMC's dramatic increase in R&D investment is correlated with the company's increase in technological advance innovation and its employment of professional human capital. Because having the mainstream technology offerings and function-rich capabilities, TSMC will be the most advanced, innovative, and largest provider of foundry services for years to come [14]. This study investigates how the diversity of the R&D source base influences an industrial performance [15] and [16]. Previous studies have focused on the impact of specific types of R&D cooperation on firm performance; the paper examines the extent to which the level of diversity of external sources of knowledge in R&D affects the performance of the focal industry. Empirical research on the effect of diversity of R&D sources on performance is scarce and results have been contradictory. Nieto and Santamaria [17] found a positive relationship between the diversity of sources and the novelty of product innovation. This study provides an important contribution by examining the role of R&D resources on industrial performance. The goal of this study is to explore the following research question: What is the nature of the relationship between diversity of R&D sources and human capital on industrial performance? Previous studies have used social identity theory and similarity/attention perspectives, particularly at the dyad and group levels of analyses, to explain the potential negative consequences of diversity on performance outcomes [18], [19], [20], [21], [22] and [23]. Knowledge-based views and decision-making perspectives have suggested that diversity promotes creativity and improves decision-making effectiveness and leads to superior performance [24] and [25].

To better understand how industries diversify their R&D external sources and internal human capital, this study examines the relationships among diversity of R&D sources, diversity of R&D human capital, innovation infrastructure, academic knowledge and how they affect industrial performance. A dataset of 315 cases of industrial level is collected to test the hypotheses. One goal of this study is to integrate a knowledge-based view of industry suggesting that knowledge heterogeneity and integration through a multiplicity of R&D resources are the keys to industrial success. Three main conclusions are summarized here: First, the results of regression analysis indicate that in general there is an inverse U-shaped relationship between diversity of R&D sources and industrial performance. The findings indicate that an optimal level of diversity of R&D sources exists. Before reaching the optimal level, the increase of diversity of R&D sources would enhance industrial performance. This result is consistent with open innovation paradigm since a greater diversity of R&D sources allows industrial firms to tap into diverse knowledge bases [30]. On the other hand, industrial performance would go down as diversity of R&D sources increases after the optimal level. The present evidences suggest that industries should realize that being too specialized or too diversified in R&D sources development strategy cannot achieve better industrial performance. Instead, an optimal level of diversity of R&D sources would exist for the industries to achieve better industrial performance due to the conciliation between the positive and negative forces governing the relationship. The managerial implications of the results are that policy managers need to know that there are two underlying governing forces on the choice of their R&D sources diversity strategy. A diversified R&D sources approach is beneficial to the industrial performance because of knowledge sharing and knowledge acquisition while a specialized R&D source approach would avoid the detrimental effects of management costs, appropriability and cognitive limit problems on industrial performance. Therefore, managers should understand that their diversity of R&D sources should be carefully defined with the appropriate approach to compromise the two governing forces in order to achieve better industrial performance. These questions are especially relevant for industries that include small and medium enterprises (SMEs), whose R&D resources are limited and need to diversify efficiently. Diverse R&D human capital also provides the conditions for industries to be able to reap the benefits of performance output. Without diversity of R&D human capital, industries may not be able to build the necessary knowledge linkages and understanding is required to efficiently make use of new innovation for industrial performance. Similarly, this study contributes empirically to our understanding of the role of diversity of R&D human capital in the industrial performance. The findings shown here also suggest that two underlying forces can explain the inverted U-shaped relationship between the diversity of R&D human capital on industrial performance outcomes. The positive part of the relationship suggests that diversity of R&D human capital is beneficial to industrial performance in terms of experience diversity and interaction between knowledge and competence. The negative part of the relationship indicates that excessive diversity of R&D human capital would be detrimental to industrial performance from the perspectives of coordination, conflicts, lower cohesions and inefficient integration. This study suggests that industrial firms need to recruit various types of R&D human capital to tap into innovation knowledge sources. However, they have to simultaneously focus their managerial attentions and resources on excessive diversity of R&D human capital so as to minimize negative costs. In doing so, they will see their industries gain in terms of industrial performance. Second, industrial diversity of R&D sources and human capital represent two rich innovation sources to characterize its industrial strategy. Composition and interrelatedness of its R&D sources and human capital diversity reveal that an industrial strategy and a series of quantifiable measures can be developed to represent the nature of an industrial technology strategy. This study develops two entropy-type indexes for R&D sources and human capital portfolios as proxies for the diversity of an industrial technology capability to measure and explain how diversity of R&D sources and human capital can have synergistic effects. For example, in this study manufacturers of basic pharmaceutical products and pharmaceutical preparations, irradiation, electromedical and electrotherapeutic equipment, and medical and dental instruments and supplies have the top three high values in diversity of R&D sources. Post hoc analysis using the Bonferroni correction also shows that all of them have significantly higher differences than other industries. Manufactures of wood and of products of wood and cork, and irradiation, electromedical and electrotherapeutic equipment have the two lowest values in diversity of R&D human capital. The multiple comparison results also show that both of them have significantly higher differences than other industries in diversity of R&D human capital. Therefore, researchers and policy managers can compare diverse strategies across different industries using numerical scales. Those quantifiable measures can also serve as policy management tools for benchmarking and technology auditing. Most importantly, they are accountable in that they can be traced to verify whether the industrial diversity of R&D sources and human capital is in line with its strategic intent. These measures can assist policy managers in diversity management and industrial performance. Third, there is the question regarding the level of diversity which is optimal for industrial progress. Answering the question will depend on several factors such as innovation infrastructure and academic knowledge. Innovation infrastructure is more helpful to industrial performance outcomes when industries adopt a higher degree of diversity of R&D sources approach while industries would obtain a better improvement effect on performance through academic knowledge when they adopt a lower degree of diversity of R&D human capital approach. This evidences implies that industries should understand that different types of moderators, like innovation infrastructure and academic knowledge, function in different ways to facilitate their performance outcomes. In Fig. 1 and Fig. 3, innovation infrastructure works as an internal maintenance mechanism that industries can reallocate these resources for buffering the heavy coordination costs and ease the process of learning. Industries that engage in a diversified R&D sources approach are likely to introduce high industrial performance if they use more innovation infrastructure as administrative resources. Conversely, the finding also suggests that industries that are considering a specialized R&D human capital strategy are likely to produce more performance if they devote more academic knowledge in the production activities. Academic knowledge is a key separator in industrial production performance and works as a facilitator of risky strategic behavior that industries should use carefully for efficient rules. Accordingly, industries with different approaches to R&D sources and human capital diversity could utilize different innovation infrastructure and academic knowledge to improve industrial performance. Therefore, researchers and policy managers can compare diverse strategies across different industries using numerical scales. Those quantifiable measures can also serve as policy management tools for benchmarking and technology auditing. Most importantly, they are accountable so that they can be traced to verify whether the industrial diversity of R&D sources and human capital are in line with its strategic intent. Those measures can assist policy managers in diversity management and industrial performance. The innovation infrastructure that supports industrial activity has broad effects on an industry. Industrial performance depends on the presence of a strong innovation infrastructure. In a broader sense, it not only includes procurement and construction for land and building, but also encompasses a country's over science and technology policy environment, the mechanisms in the place for supporting basic research and higher education, intellectual property protection, the rate of taxation of R&D and capital gains, the cumulative stock of technological knowledge and institutional aspects such as subsidies and other governmental inhibiting or stimulating policies. It is important to note that the innovation infrastructure incorporates both the overall scale of innovation inputs within an industry as well as industry-wide sources of R&D productivity [79]. At a high level of diversity of R&D sources, industrial performance depends on the strength of the linkages between innovation infrastructure and specific industrial R&D sources. A higher innovation infrastructure raises more productive flow of innovative output and thus induces industrial performance. In Fig. 1 and Table 5, for innovation infrastructure to be effective, it is important to investigate which industries have high values in diversity of R&D sources. For example, in manufacturers of basic pharmaceutical products and pharmaceutical preparations, irradiation, electromedical and electrotherapeutic equipment and medical and dental instruments and supplies, when policy makers consider innovation infrastructure as an important means to moderate between diversity of R&D sources and industrial performance, the expected higher benefits of diversity of R&D sources for industrial growth might be achieved. Academic knowledge needs a prolonged process of accumulation and transfer. Therefore, policies to support collaboration between academic institution and firms in the industry and create incentives for both sets of actors to knowledge transfer are extremely important. Policy makers need to assist efforts to preserve and accumulate the well-developed academic knowledge capacity residing in industry in order for industries to assimilate the targeted new knowledge more effectively for future growth. In addition, the government's role is to facilitate the investment of new innovation infrastructure within industries and industrial policy is encouraged to design and implement infrastructure that ease the innovative in an industry [80]. Industrial performance enhances a nation's competitiveness. By supporting performance-related activities in creating and upgrading factors such as R&D research institutions, human capital, innovation infrastructure and academic knowledge, the government can also lead an industry in a proper direction rapidly. That is to say, established policies must evolve to reflect the shifting innovative effects of the diversity of R&D sources and human capital in an industry. Based on Fig. 3, for low values of diversity of R&D human capital, the results show that the level of academic knowledge matters and the difference on industrial performance exists. For example, in Table 5 the two lowest values of diversity of human capital were found in manufacturers of wood and of products of wood and cork and irradiation, electromedical and electrotherapeutic equipment. The lower the value of diversity of R&D human capital in these two industries, then academic knowledge plays a key trigger and derives higher industrial performance. Both innovation infrastructure and academic knowledge work as facilitators of risky strategic behavior that policy makers should use both carefully for efficient rules. Accordingly, industries with different approaches to R&D sources and human capital diversity could utilize different innovation infrastructure and academic knowledge to improve industrial performance. Therefore, researchers and policy managers can compare diverse strategies across different industries using numerical scales. Those quantifiable measures can also serve as policy management tools for benchmarking and technology auditing. Most importantly, they are accountable so that the benchmarks can be traced to verify whether the industrial diversity of R&D sources and human capital are in line with its strategic intent. Those measures can assist policy managers in diversity management and industrial performance. Finally, this study provides an empirical framework in which the effects of diversity of R&D sources and diversity of R&D human capital are estimated over a time interval of 9 years. The complex and specific nature of knowledge embedded in the technologies and practices in the R&D sources and R&D human capital require that firms in an industry take sufficient time to translate complementary know-how into a novel innovation. In order to take into account the existence of a set of feedback mechanisms and of the dynamic co-evolution among the different variables considered here. In particular, it would be reasonable to expect that the effects of diversity of R&D sources and diversity of R&D human capital will affect the transformation of industrial systems of innovation in the long run, in turn, the productivity and economic performance of industrial sectors [81]. From knowledge-based perspective, transferring complex and causally ambiguous knowledge typically requires reconstruction and adaptation processes. Long time intervals enable R&D sources and R&D human capital to influence the ease of how they can combine existing and new knowledge [82], especially if this knowledge requires modification to fit the new application or context [83]. In sum, increasing industrial R&D efforts should stimulate innovation with some time lag [84] and [85] and therefore, enhance industrial productivity. By measuring industrial fluctuations, such as growth in demand, public policy, economic cycles, year-to-year variations in productivity, of the returns on diversity of R&D sources and human capital over a long time interval, and by allowing for an endogenous lag structure between diversity of R&D sources and human capital and their effects on industrial performance. The effects of institutional changes could require time to realize and R&D is assumed to require some time to commercialize to product or process development. This study uses a 9 years period of record.